Effect of Artificial Intelligence on Mathematics Performance: A Meta-Analysis

The rapid advancement of artificial intelligence (AI) has profoundly reshaped global educational paradigms. By leveraging its well-established education system, Finland has actively integrated AI into physical education (PE), driving significant advancements in school-based PE. Meanwhile, China’s PE reform is at a critical juncture, facing challenges in resource allocation, pedagogical innovation, and technological integration. This study systematically examines AI applications in Finnish PE, evaluates their pedagogical impact, and explores their applicability in the Chinese context to inform AI-driven innovations in China’s PE reform. A systematic literature review focused on AI applications in PE within Finland and China. Data were retrieved from Web of Science, Scopus, Google Scholar, and CNKI, covering English and Chinese publications from 2014 to 2024. The study adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, with thematic content analysis identifying key areas of AI implementation. The findings reveal that Finland has successfully integrated AI into PE, primarily in the following areas: (1) Personalized and Adaptive Learning – AI platforms dynamically adjust training regimens based on students’ physical conditions and learning progress. AI-powered interactive games further enhance student engagement. (2) Performance Analytics and Feedback – The Finnish National Agency for Education collaborates with AI enterprises, utilizing AI-driven data analytics to optimize sports performance and injury prevention. (3) Teacher Training in AI Pedagogy – Finnish educators undergo systematic training in AI-assisted PE instruction, with institutions such as the University of Jyväskylä prioritizing AI competency in teacher preparation. China has initiated pilot projects in AI-powered sports analytics and digital fitness education, reflecting a growing emphasis on AI-driven PE. However, challenges persist, including disparities in technological resources, insufficient AI literacy among educators, and outdated assessment frameworks. AI holds transformative potential for PE, and Finland’s experience offers valuable insights for China’s PE reform. Key recommendations include: (1) Enhancing AI Integration in Teacher Training – Strengthening AI-focused professional development to equip educators with necessary competencies. (2) Advancing AI-Driven Performance Assessment – Utilizing AI technologies to enable objective, data-informed evaluations. (3) Fostering Policy Support for AI Adoption – Promoting AI-integrated pedagogical models to drive innovation in PE instruction. (4) Bridging the Digital Divide – Addressing infrastructural disparities to ensure equitable access to AI-enhanced PE education. Future research should empirically assess the efficacy of AI applications in Chinese PE and their impact on student learning outcomes.

Background: Artificial intelligence (AI) has been proposed as a transformative tool in suicide prevention, yet most evidence remains observational. To provide a rigorous benchmark, we systematically reviewed randomized controlled trials (RCTs) evaluating AI-based interventions targeting suicidal thoughts, behaviours, or help-seeking. Methods: Following PRISMA 2020 guidelines, MEDLINE, Web of Science, and Scopus were searched to 31 May 2025. Eligible studies were RCTs in humans that incorporated AI or machine learning for risk prediction, automated intervention, or treatment allocation. Methodological quality was assessed with the PEDro scale and certainty of evidence with GRADE. Results: From 1101 screened records, six RCTs (n = 793) met all criteria. Three studies tested machine learning risk prediction, two evaluated fully automated interventions (a transformer-based recommender and a digital nudge), and one examined AI-assisted treatment allocation. Risk-prediction models stratified short-term suicidal outcomes with accuracies of up to 0.67 and AUC values around 0.70. Digital interventions reduced counsellor response latency or increased crisis-service uptake by 23%. Algorithm-guided allocation reduced the occurrence of suicidal events when randomisation aligned with model recommendations. Methodological quality was moderate to high (median PEDro = 8/10), but GRADE certainty was low due to small samples and imprecision. Conclusions: AI can enhance discrete processes in suicide prevention, including risk stratification, help-seeking, and personalized treatment. However, the current evidence is limited, and larger multisite RCTs with longer follow-up, CONSORT-AI compliance, and equity-focused design are urgently required.

This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

The advancement of artificial intelligence (AI) technology has brought significant transformations across various sectors, including the judicial system. Countries such as China and the United States have adopted AI in legal decision-making, whereas Indonesia remains in the early stages of implementing this technology. While AI has the potential to enhance judicial efficiency, challenges related to regulation, transparency, and algorithmic bias raise concerns regarding the accuracy and fairness of legal rulings. Therefore, this study aims to analyze the effectiveness of AI utilization in Indonesia’s judicial system, identify key challenges, and propose policy recommendations. This research employs both normative and empirical approaches by examining AI-related regulations and conducting interviews with judges and technology experts in Indonesia. The findings indicate that 65% of judges in Indonesia remain skeptical about AI's role in legal decision-making. In contrast, in countries like China, AI adoption in the judiciary has improved case resolution efficiency by 30–40%. Additionally, a public survey reveals that while 55% of respondents believe AI can enhance the objectivity of legal rulings, 45% remain concerned about potential algorithmic bias. Furthermore, the study highlights that Indonesia lacks specific regulations governing AI in the legal system, which may hinder the widespread adoption of this technology. This research contributes to understanding the readiness of Indonesia’s judicial system in integrating AI and emphasizes the necessity of clear regulations and robust oversight mechanisms to ensure transparency and accountability. The findings are expected to serve as a foundation for policymakers in formulating comprehensive regulations to optimize AI implementation in the Indonesian judicial system

The research paper discusses the impact of Artificial Intelligence (AI) on organizational and process-level performance. The study follows a four-step process: analyzing AI technologies, exploring case studies, collecting data, and reviewing AI literature. The findings indicate that AI has various technologies, such as machine translation, chatbots, and self-learning algorithms, that can enhance automation, information, and transformation effects. Organizations can use AI to improve processes, optimize operations, and develop strategic and competitive advantages. The study provides insights into how organizations can enhance the business value of their transformation projects by leveraging AI's attributes. The research framework proposes a more comprehensive approach to account for the intangible benefits of AI in organizations. The study highlights that AI combines several configurations of IT in various industries, and organizations should reconfigure their processes to achieve performance through AI capabilities. The research also provides tangible evidence about the business value of AI-based projects and their impact on firm performance, considering it not as a single technology but as a set/combination of several different configurations of IT in various industries. The study has scientific and managerial interests and proposes a model for analyzing the impact of AI on firm performance, providing managers with insights to improve their organizations' performance, profitability, and competitive advantage.

This study aims to determine the factors influencing the students' mathematics performance in higher education in Riau Province. The research population was all students in higher education in Riau Province majoring in mathematics. The samples of this research were some students in higher education majoring in mathematics education which were taken randomly with a proportional random sampling approach. The instrument in this research was a questionnaire developed and validated by experts and distributed to mathematics education students. The data analysis used in this research was path analysis. The results showed that there was an effect of learning motivation on mathematics performance, there was no effect of learning interest on mathematics performance, with a T Value of 1.28, (12) there was no effect of self-efficacy on mathematics performance, with T Values of 1.38, there was an effect of self-regulated on mathematics performance, with T Values of 2.23, and (15) there was no effect of learning involvement on mathematics performance, with T Value of 1.39 The dominant and significant factor in influencing the students' mathematics performance in higher education in Riau province during the COVID-19 Pandemic was learning motivation and self-regulation.

Foreign language anxiety (FLA) has been a perennial concern in language learning, as foreign language (FL) learners often communicate feelings of anxiety, stress, or nervousness. This study explored the role of artificial intelligence (AI) applications in speaking practice for FLA management of 48 undergraduate participants in an EFL class in Egypt. An eight‐week, quasi‐experimental pretest–posttest design examined learner anxiety levels using a 33‐item FLA questionnaire. Their oral proficiency was assessed via roleplaying using an interaction‐enhanced public version of the IELTS speaking evaluation rubric. The results confirmed that FL learners experienced FLA pre‐ and post‐intervention. The identified anxiety levels played a facilitative role in FL learning with several ensuing gains. The use of conversationally enhanced AI chatbots in interactive activities slightly intensified learners' FLA, thus meriting further investigation of these objectives. Overall, subject to further development, AI chatbots are promising for significantly improving linguistic output gains; however, this study found that learners' speech‐related anxieties were not reduced following the interactions with the chatbots. It is concluded that FLA plays an underexplored facilitative role in sharpening learner cognitive faculties and linguistic capacities. Moreover, AI chatbots may be beneficial in advancing FL learning with significant potential in EFL contexts, facilitating improved interaction and oral communication. These findings support the integration of AI technologies as effective tools in FL education, providing flexible, interactive, and learner‐centred learning. This study is expected to be of considerable interest to FL educators and learners.

Artificial Intelligence (AI) is increasingly becoming a cornerstone in various sectors of life, including education, where it significantly enhances learning experiences.As we advance technologically, integrating AI into educational strategies is more common, marking a shift towards innovative and adaptable educational methods.This integration signifies a deep transformation aimed at reshaping the foundational ways of delivering and receiving knowledge.Central to this evolution is the ability to offer personalized learning experiences.AI employs sophisticated algorithms to understand each learner's preferences, strengths, and weaknesses, enabling the crafting of tailored learning paths for every student.This approach moves beyond the traditional one-size-fits-all method, catering to the diverse needs and goals of students.However, incorporating AI into education is not without its challenges.Teachers face the task of seamlessly blending AI technologies into their teaching practices.Furthermore, the rise of substantial ethical concerns calls for careful consideration of the potential risks and implications of using AI in educational settings.Finding a balance between leveraging AI's benefits and addressing ethical issues is essential for its responsible and impactful use in education.This discussion explores AI's role in changing educational frameworks, highlighting its ability to revolutionize teaching methods and promote personalized learning.Understanding the advantages, challenges, and ethical considerations of AI in education prepares educators to utilize AI's power to enhance educational outcomes effectively.

Artificial intelligence (AI) is revolutionizing education, improving personalized learning, automating administrative tasks and increasing student engagement. As AI-based technologies such as machine learning, natural language processing, and adaptive learning systems evolve, their integration into educational environments is transforming traditional teaching methods. This article explores the impact of artificial intelligence on education, analysing both its potential benefits and challenges. The primary data collection tool was an online survey, developed and distributed via Google Forms during spring 2025. The questionnaire included rating scales, single-choice, and multiple-choice questions focused on familiarity and usage of AI, perceived benefits and risks, and evaluations of effectiveness. A total of 100 respondents, primarily drawn from higher education (students, teachers, academic staff) and supplemented by participants from secondary education. This sample reflects a variety of roles and educational backgrounds, ensuring a multi-perspective view. The survey explored knowledge and practical experience with AI-powered tools in education; effectiveness of specific tools (chatbots, adaptive systems, automated grading, etc.); and perceptions of benefits, challenges, ethical issues, and prospects for AI integration. The research used descriptive statistics to interpret results. Most respondents were familiar with AI and had used AI-powered tools in educational settings. Most agreed that AI positively impacts education, especially in areas like personalized and adaptive learning, automated grading and feedback, chatbots for student support, data-driven performance prediction, and reducing administrative workload for teachers. At the same time, concerns about privacy, bias, decreased human interaction, inequality of access, and over-reliance on technology were consistently rated as significant. These findings demonstrate that while AI is widely seen as beneficial, its integration must be managed thoughtfully to address associated risks.

Artificial intelligence has sophisticated social and economic effects that cannot be ignored. Based on a thorough review of the development of artificial intelligence, this paper systematically explores the mechanism of the impact of artificial intelligence on economic growth through technology, value and application three paths, which is starting from the perspective of the population external system. In order to verify the rationality of the paths, the effect of artificial intelligence on economic growth from the perspective of population external system is rigorously estimated using artificial intelligence and macroeconomic data for China from 2011 to 2019. The findings are as follows. Firstly, there is a significant positive effect of artificial intelligence on the economic growth from the perspective of the population external system. This positive effect is sufficiently robust over the sample-wide period. Secondly, there is significant regional heterogeneity in the effect of artificial intelligence on economic growth from the perspective of the population external system. The low levels of artificial intelligence development impeded the economic growth, the middle levels of artificial intelligence development contributed significantly to the economic growth, and the high levels of artificial intelligence development did not show a significant contribution to the economic growth. In view of this, future policies should be designed in terms of revitalizing the value of the artificial intelligence stock, exploring the value potential of artificial intelligence and regulating it in a hierarchical manner.

The 21st century has embraced many innovations as the year of high technology. With this developing high technology, as the list of things to do on the internet and computers increases, new applications and programs made with artificial intelligence increase. Especially in parallel with the development of computer technology, computers have learned to draw pictures, play games and chat better than humans with the word commands they write to the artificial intelligence bot. There are many applications that have emerged in this sense. Within the framework of these developments, the needs have diversified and this situation is reflected in the professions. Applications produced with artificial intelligence, while reducing the need for some professions, also allowed the creation of new professions. In this sense, it is entirely up to the user experience how the graphic design field, which is seen as one of the professions where the need has decreased, will use and develop artificial intelligence. The use of artificial intelligence and technology in the field of graphic design has been discussed more and more in recent years. At the center of these discussions is the role and impact of artificial intelligence and technology in the graphic design process. On the one hand, the use of artificial intelligence and technology can speed up the design process and enable to work more efficiently, on the other hand, it can cause concerns in terms of creativity and aesthetics of design. In particular, concerns are expressed about the designs produced with artificial intelligence-based design tools and the dependence of the designers on their creativity and the originality of these designs. Within the scope of this research, recent applications were examined regarding search answers for the questions of how can graphic design field, graphic designer and graphic design education be affected in this sense? How does it affect creativity? What can images produced by artificial intelligence with our knowledge based on visual culture teach us?. The findings of the research reveal designers working in the field of graphic design have to constantly renew and update themselves in order to follow the innovations in the field. In addition, they should be careful that the designs they make with the applications they use are not an ethical problem. Therefore, while the use of artificial intelligence and technology in the field of graphic design is expected to increase, more research is needed on the effects of artificial intelligence and technology use in the design process and its usage areas.

Artificial intelligence is profoundly influencing various facets of our lives, indicating its potential to significantly impact sustainability. Nevertheless, capturing the productivity gains stemming from artificial intelligence in macro-level data poses challenges, leading to the question of whether artificial intelligence is reminiscent of the “Solow paradox”. This study employs micro-level manufacturing data to investigate the impact of artificial intelligence on firms’ productivity. The study finds that every 1% increase in artificial intelligence penetration can lead to a 14.2% increase in total factor productivity. This conclusion remains robust even after conducting endogeneity analysis and a series of robustness tests. The study identifies that the positive impact of artificial intelligence on productivity is primarily achieved through the value-added enhancement effect, skill-biased enhancement effect, and technology upgrading effect. Furthermore, the study reveals that the effects of artificial intelligence on productivity vary across different property rights and industry concentration contexts. Additionally, the structure of factor endowments within firms can also influence the productivity gains from artificial intelligence. Our study presents compelling evidence demonstrating the role of artificial intelligence in fostering economic sustainability within the framework of Industry 4.0.

ABSTRACTAutomation and artificial intelligence technology have played a pivotal role in today’s economic and social development. They represent a labor-substituted technological progress, featuring more and more jobs to be replaced by AI. Based on the adoption rate calculated in our paper and theoretical substitution probability estimated by existing studies, our research estimates the actual substitution probability by AI for various occupations in China. By using this actual substitution probability on occupation level, we also explore the substitution effects on labor force with different characteristics and find that AI has larger substitution impacts on labors of female, old age, low education and low income. We also predict the number of employed people that would be replaced by AI in each industry, and the results show that China will have 278 million labors (201 ~ 333 million under different adoption rates) replaced by AI by 2049, representing 35.8% of the current employment in China.Abbreviation: Artificial intelligence (AI), internet of things (IoT), Council of Economic Advisers (CEA)

This paper introduces the STOP method, a replicable framework designed to guide educators in creating clear, precise prompts tailored to their needs. Artificial intelligence (AI) platforms are revolutionizing education by enhancing teaching methodologies, streamlining administrative tasks, and fostering deeper student engagement. These tools generate content with remarkable efficiency, freeing educators to focus on instruction and student support. However, maximizing AI’s potential requires understanding its capabilities and crafting effective prompts. Each element of the STOP method ensures that AI-generated outputs align with instructional goals and contextual demands. Through detailed explanations and practical examples, this paper demonstrates how educators can efficiently produce diverse resources, such as lesson plans, presentations, and assessments, using AI as a transformative tool in education.

Artificial intelligence (AI) has emerged as a transformative tool in veterinary diagnostic microbiology, enabling the analysis of complex datasets generated by modern laboratory technologies. This study aimed to review current applications of AI in veterinary microbiology, with emphasis on its relevance within the One Health framework. A narrative literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar. Studies addressing machine learning and deep learning applications in microbial identification, antimicrobial resistance detection, genomic analysis, and infectious disease surveillance were included following structured screening and eligibility criteria. The findings indicate that AI is increasingly applied across multiple domains of veterinary diagnostic microbiology. Key applications include automated microbial identification using MALDI-TOF mass spectrometry, image-based diagnostics through deep learning, genomic and metagenomic analysis for pathogen detection, and prediction of antimicrobial resistance. AI has also demonstrated value in clinical decision support and in integrating microbiological, epidemiological, and environmental data for disease surveillance within the One Health context.However, important limitations persist, including limited availability of standardized veterinary datasets, heterogeneity across species and diagnostic systems, and challenges related to model interpretability, infrastructure, and regulatory frameworks.In conclusion, AI represents a promising tool to enhance diagnostic accuracy, optimize laboratory workflows, and strengthen disease surveillance. Addressing current limitations will be essential to support its effective and responsible implementation in veterinary microbiology.